Power system transient stability margin estimation using neural networks

This paper proposes a methodology for estimating a normalized power system transient stability margin (Delta V-n) using multi-layered perceptron (MLP) neural network with a fast training approach. The nonlinear mapping relation between the Delta V-n and operating conditions of the power system is established using the MLP neural network. The potential energy boundary surface (PEBS) method along with a time-domain simulation technique is used to obtain the training set of the neural network. Results on the New England 10-machine 39-bus system demonstrate that the proposed method provides a fast and accurate tool to evaluate online power system transient stability with acceptable accuracy. In addition, based on the examination of generators rotor angles after faults, a method is presented to select the power system operating conditions that most effect the Delta V-n for each fault. (C) 2011 Elsevier Ltd. All rights reserved.